Parameters of 150 temperate and boreal tree species and provenances for an individual-based forest landscape and disturbance model

Understanding the impacts of changing climate and disturbance regimes on forest ecosystems is greatly aided by the use of process-based models. Such models simulate processes based on first principles of ecology, which requires parameterization. Parameterization is an important step in model development and application, defining the characteristics of trees and their responses to the environment, i.e., their traits. For species-specific models, parameterization is usually done at the level of individual species. Parameterization is indispensable for accurately modeling demographic processes, including growth, mortality, and regeneration of trees, along with their intra- and inter-specific interactions. As it is time-demanding to compile the parameters required to simulate forest ecosystems in complex models, simulations are often restricted to the most common tree species, genera, or plant-functional types. Yet, as tree species composition might change in the future, it is important to account for a broad range of species and their individual responses to drivers of change explicitly in simulations. Thus, species-specific parameterization is a critical task for making accurate projections about future forest trajectories, yet species parameters often remain poorly documented in simulation studies. We compiled and harmonized all existing tree species parameters available for the individual-based forest landscape and disturbance model (iLand). Since its first publication in 2012, iLand has been applied in 50 peer-reviewed publications across three continents throughout the Northern Hemisphere (i.e., Europe, North America, and Asia). The model operates at individual-tree level and simulates ecosystem processes at multiple spatial scales, making it a capable process-based model for studying forest change. However, the extensive number of processes and their interactions as well as the wide range of spatio-temporal scales considered in iLand require intensive parameterization, with tree species characterized by 66 unique parameters in the model. The database presented here includes parameters for 150 temperate and boreal tree species and provenances (i.e., regional variations). Excluding missing values, the database includes a total of 9,249 individual parameter entries. In addition, we provide parameters for the individual susceptibility of tree species to wind disturbance (five parameters) for a subset of 104 tree species and provenances (498 parameter entries). To guide further model parameterization efforts, we provide an estimate of uncertainty for each species based on how thoroughly simulations with the respective parameters were evaluated against independent data. Our dataset aids the future parameterization and application of iLand, and sets a new standard in documenting parameters used in process-based forest simulations. This dataset will support model application in previously unstudied areas and can facilitate the investigation of new tree species being introduced to well-studied systems (e.g., simulating assisted migration in the context of rapid climate change). Given that many process-based models rely on similar underlying processes our harmonized parameter set will be of relevance beyond the iLand community. Our work could catalyze further research into improving the parameterization of process-based forest models, increasing the robustness of projections of climate change impacts and adaptation strategies.

a b s t r a c t Understanding the impacts of changing climate and disturbance regimes on forest ecosystems is greatly aided by the use of process-based models.Such models simulate processes based on first principles of ecology, which requires parameterization.Parameterization is an important step in model development and application, defining the characteristics of trees and their responses to the environment, i.e., their traits.For species-specific models, parameterization is usually done at the level of individual species.Parameterization is indispensable for accurately modeling demographic processes, including growth, mortality, and regeneration of trees, along with their intra-and inter-specific interactions.As it is time-demanding to compile the parameters required to simulate forest ecosystems in complex models, simulations are often restricted to the most common tree species, genera, or plant-functional types.Yet, as tree species composition might change in the future, it is important to account for a broad range of species and their individual responses to drivers of change explicitly in simulations.Thus, speciesspecific parameterization is a critical task for making accurate projections about future forest trajectories, yet species parameters often remain poorly documented in simulation studies.We compiled and harmonized all existing tree species parameters available for the individual-based forest landscape and disturbance model (iLand).Since its first publication in 2012, iLand has been applied in 50 peer-reviewed publications across three continents throughout the Northern Hemisphere (i.e., Europe, North America, and Asia).The model operates at individual-tree level and simulates ecosystem processes at multiple spatial scales, making it a capable processbased model for studying forest change.However, the extensive number of processes and their interactions as well as the wide range of spatio-temporal scales considered in iLand require intensive parameterization, with tree species characterized by 66 unique parameters in the model.The database presented here includes parameters for 150 temperate and boreal tree species and provenances (i.e., regional variations).Excluding missing values, the database includes a total of 9,249 individual parameter entries.In addition, we provide parameters for the individual susceptibility of tree species to wind disturbance (five parameters) for a subset of 104 tree species and provenances (498 parameter entries).To guide further model parameterization effort s, we provide an estimate of uncertainty for each species based on how thoroughly simulations with the respective parameters were evaluated against independent data.Our dataset aids the future parameterization and application of iLand, and sets a new standard in documenting param-eters used in process-based forest simulations.This dataset will support model application in previously unstudied areas and can facilitate the investigation of new tree species being introduced to well-studied systems (e.g., simulating assisted migration in the context of rapid climate change).Given that many process-based models rely on similar underlying processes our harmonized parameter set will be of relevance beyond the iLand community.

Value of the Data
• Tree species parameters were obtained and harmonized (e.g., updating multiple versions of species parameters to the latest version) from research groups who have used the individualbased forest landscape and disturbance model (iLand) [ 1 ] across three continents and nine countries (Austria, Belgium, Canada, Czechia, Germany, Finland, Japan, Slovakia, and USA).
The dataset [ 2 ] contains a total of 9249 entries for 66 parameters of 150 tree species and provenances from the temperate and boreal biomes.The parameters characterize the growth, survival (or mortality), and regeneration of trees within iLand as well as the simulated carbon and nitrogen dynamics.• A second database [ 2 ] includes parameters addressing the susceptibility of trees to wind disturbance.This database includes a total of 498 entries for five parameters of 104 tree species and provenances.• Tree species parameter sets were categorized into three uncertainty categories to indicate how thoroughly simulations of these species were evaluated against independent data.We identified 14 high confidence tree species parameter sets, 89 parameter sets with medium confidence, and 47 parameter sets with low confidence.
• The database facilitates the simulation of previously unstudied areas by providing a starting point for parameter testing and refinement.It furthermore allows the simulation of a wider set of tree species in existing study areas (e.g., to study assisted migration in the context of rapid climate change).Both databases presented here are ready to use in iLand.Since many parameters are relevant also in the context of other models the database has relevance for the forest modeling community.

Background
One important step in process-based modeling is to establish a set of parameters that characterize the simulated entities (here: trees), their responses to the environment, and their interand intra-specific interaction with other trees.Researchers have derived parameters for multiple species from various regions growing under a wide range of environmental conditions.They furthermore have evaluated simulations performed with these parameters against independent data sets characterizing specific aspects of the focal study system.By compiling and harmonizing the parameters from these different systems and sources, we synthesize the currently available work on characterizing temperate and boreal tree species in iLand, with the aim to improve model parameter reusability within the community, and to facilitate future model parameterization and application.

Data Description
The data are available as tables within an SQLite database file [ 2 ].SQLite is an open-source database compatible with iLand and analysis tools like R [ 3 ].The first table ("species") encompasses all species parameters used in iLand for simulating demographic processes and environmental responses as well as carbon and nitrogen cycling.The second table ("wind") specifically focuses on parameters defining the response of trees to wind disturbance.The structure of both tables is described in Tables 1 and 2 , respectively.
The 150 species and provenances included in the database exhibit very different levels of similarity based on their species parameter values ( Fig. 1 ).Broadleaved and coniferous tree species are clearly separated by their parameters, with few exceptions (i.e., deciduous conifers such as Larix laricina and Larix kaempferi ).Moreover, clusters are clearly separated by continent.The most similar species are Quercus robur and Quercus petraea , whereas dissimilarity was highest between Castanea sativa and Pinus contorta (high elevation variety with serotinous cones).

Experimental Design, Materials and Methods
The derivation of tree species parameters for process-based modeling is a time and resource intensive process that includes the compilation of an initial set of parameters (e.g., from the literature), followed by an iterative process of evaluation and refinement, ensuring that the parameters are consistent with the internal model logic, and that they reproduce the patterns expected for the simulated ecosystem [ 4 ] ( Fig. 2 ).Here, we report parameters for the individualbased forest landscape and disturbance model (iLand) [ 1 ].Introduced in 2012, iLand is an innovative process-based model for simulating the interactions among individual trees and their environment across a hierarchy of spatio-temporal scales, spanning from individual trees to the landscape and from minutes to millennia.iLand is based on first principles of ecology and is built around the representation of a multitude of ecosystem processes and their interactions.This process-based architecture enables robust projections of forest and disturbance dynamics also under changing environmental conditions.iLand has been successfully employed in temperate and boreal forests across Europe, North America, and Asia.For example, iLand has been used Table 1 Names, descriptions and examples of tree species parameters used in iLand to characterize trees and simulate their demographic processes, environmental response, as well as carbon and nitrogen dynamics.Each row refers to a speciesspecific parameter in the SQLite database (   The annual rate at which the biomass of litter decomposes.This rate depends on species and is modified by environmental conditions (i.e., temperature and moisture).

snagKYR
The annual rate at which the biomass of downed woody debris decomposes.This rate depends on species and is modified by environmental conditions (i.e., temperature and moisture).

Table 2
Names, descriptions and examples of tree species parameters used in iLand to simulate the response of trees to wind disturbance.Each row refers to a species-specific parameter in the SQLite database (Table wind).For details on the use of the parameters in the iLand model logic see the online model documentation at https://iland-model.org .

Parameter name Description Example
CReg Critical turning coefficient (Nm kg −1 ) derived from tree pulling experiments.

crownAreaFactor
Empirical factor for the crown shape (fraction of area of the projected crown shape compared to a rectangle).Parameter similarity among the tree species and provenances included in the dataset.The phylogram is based on an Agglomerative Hierarchical Clustering using a Gower distance matrix of 54 species parameters (i.e., those which could be meaningfully included in the analysis from the overall 66 parameters) for 150 tree species and provenances.The R code for the analysis can be accessed here: https://github.com/DominikThom/iLand-Species-Parameters.git .

Fig. 2.
The steps to derive a robust species parameter set for process-based modeling.First, an initial parameter set is compiled from multiple sources.Subsequently, different patterns of ecosystems are simulated and evaluated against independent observations.Parameters might need to be iteratively adjusted (while ensuring that the parameter value remains within an ecologically plausible range), but local overfitting should be avoided to ensure realistic responses to novel environmental conditions.
to simulate forest restoration in Asia [ 5 ], forest dynamics under climate change in Europe and North America [ 6 , 7 ] and disturbance regime shifts under climate change in Europe and North America [ 8 , 9 ] as well as changes in ecosystem services [ 10 ] and biodiversity [ 11 ] in Europe.
The parameters compiled here form the backbone of iLand simulation studies.They have been generated by the research community in a variety of ways and from numerous sources.We here briefly describe a default approach to estimating model parameters in the context of iLand, but acknowledge that the process can deviate substantially in individual cases as data availability for parameterization varies.Initial parameters are usually based on a combination of measurements, literature values, and expert estimates.Parameterization thus draws upon diverse data sources.We suggest to begin the parameterization by using observational data to derive species parameters (e.g., national forest inventories).Parameters that cannot be obtained from observational data might be found in species trait databases (e.g., the TRY database [ 12 ]).More parameters might be found in the (recent) peer-reviewed literature (e.g., [ 13 ]) or grey literature (e.g., [ 14 ]).If individual parameters are not available for a species of interest, expert knowledge (e.g., estimations based on the parameters of a closely ecologically related species) is frequently leveraged to fill gaps (see e.g.Fig. 1 ).
Initial parameters subsequently require careful refinement to ensure that they make up a coherent species parameter set that results in the emergence of realistic trajectories in the simulation.This refinement entails the thorough evaluation of the simulation results obtained with the respective parameters.Iteratively adjusting species parameters based on repeated analysis of model outputs and their comparison to independent data may be needed (see Fig. 2 ).We advocate for a pattern-oriented approach to model testing [ 4 ].This involves comparing model outputs against both quantitative and qualitative information available for a study system.Given that iLand operates across multiple hierarchical scales, evaluation should also consider multiple scales.Depending on data availability, model evaluation focuses on: -Individual-tree level: • Tree dimensions (e.g., average and distribution of diameter at breast height (dbh) and tree height) for each species.This is usually well documented from historical observations or can be obtained from old-growth forests.• Climate sensitivity (e.g., annual growth anomalies of trees).This can be obtained from regular measurements of tree growth (e.g., diameter increment from dendrometers).• Tree competition (e.g., growth response to tree neighbourhood).This can be evaluated against data from silvicultural trials (e.g., thinning or spacing experiments).-Stand level: • Stand productivity (e.g., increment in: volume, basal area, dbh, and height).This can be tested for single-species stands and for stands with a mix of different species.Data for comparison can be obtained from local forest inventories and yield tables. • Environmental responses (e.g., changes in growth, mortality, and regeneration due to water stress).Data for comparison can, for instance, be derived from permanent forest monitoring plots or eddy covariance flux towers, but can also include the comparison of model behaviour across wide environmental gradients (e.g., across elevation).• Species competition and dominance (e.g., growth, mortality, and regeneration in species mixtures).Simulations can be compared with periodic inventories as well as species mixture trials from growth and yield studies. -Landscape level: • Potential natural vegetation (i.e., the natural succession of species towards a tree species composition that is in dynamic equilibrium with the prevailing climatic conditions in the absence of human intervention).Simulations can be compared with local floristic assessments of forest types and expert estimates (e.g., gradients in species dominance across an elevational gradient), and can also use observations from unmanaged forests.The evaluation can focus on both the dynamic equilibrium species composition after a long simulation period but also the trajectory to this dynamic equilibrium, evaluating the simulated transition from early seral to late seral species over time.

Table 3
Confidence levels in the tree species parameters compiled here.Tree species parameter sets are categorized into high, medium, or low confidence.These confidence levels are primarily derived from the level of evaluations conducted for a species: Species evaluated across a broad range of environmental conditions against diverse sets of independent data are classified as high confidence.Species evaluated locally against limited data are rated as medium confidence, and species for which parameters have been compiled but have not been evaluated, yet, are deemed low confidence.Provenances indicated in square brackets.
( continued on next page ) • Species migration rate (i.e., the movement of species across the landscape).Comparisons be based on paleo records or terrestrial observations in response to ongoing climatic changes.
• Disturbance regime (e.g., disturbance rates, sizes, frequencies, interactions etc.).Comparison of natural disturbance patterns and effects on the tree vegetation and subsequent regeneration can be performed based on remote sensing data, terrestrial inventories or other field data.
iLand is a process-based model based on first principles in ecology.Hence a site-specific adjustment of parameters is not recommended unless the performance of simulations in other regions increases simultaneously, as it could lead to local overfitting of parameters, reducing the robustness in applications under global change conditions.Rather, the parameters should broadly represent species in the simulation across a range of conditions, in some instances trading off precision for accuracy in simulated outcomes.For some species occurring under a very wide range of conditions, or for specific applications of the model, it is meaningful to distinguish individual tree species provenances in model parameterization (e.g., boreal vs. temperate Pinus sylvestris ).The current dataset contains 21 provenances for nine tree species.Most parameters compiled here underwent initial testing and evaluation ( Fig. 2 ).However, the effort used and data available for evaluation varies considerably among species, and species are added and refined with the growing use of iLand.To communicate the resultant degrees of confidence in the parameterization of a tree species transparently, we assigned three categories ( Table 3 ).Species parameter sets evaluated across a broad range of environmental conditions against diverse sets of independent data are classified as high confidence, those evaluated locally against limited data are rated as medium confidence, and those compiled but not evaluated are deemed low confidence.

Limitations
• Only a few tree species and provenances contained in the database presented here have been thoroughly evaluated.The large majority of tree species and provenances parameters have moderate to low confidence and require further evaluation ( Table 3 ).• Parameters for rare species are frequently less robust due to fewer studies of species traits and limited independent data for evaluation ( Table 3 ).• Few provenances within species have been parameterized.Apart from these provenances, intra-specific variation in parameters is not considered in iLand.• With the exception of regeneration parameters, average traits across a tree's life span are used within the simulation, although some traits may vary considerably with tree age.• The tree traits reported here need to be interpreted within the context of the iLand model logic.• Independent data is often lacking to thoroughly evaluate individual processes in the simulation and their underlying parameters.

2 . 2 phenologyClass 5 nonSeedYearFraction
Link to a phenology class.0 = evergreen coniferous, 1 = deciduous broadleaved, 2 = deciduous coniferous.0 maxCanopy Conductance Maximum conductance of the canopy for water (m s −1 ).Used in the calculation of transpiration.0.02 psiMin Maximum soil water potential (MPa) that a species can access (i.e. a species' permanent wilting point).−1.5 maturityYears Minimum age (years) required for a tree to produce seeds.30 seedYearInterval Interval between seed (masting) years.Each year has a probability of 1/seedYearInterval that a year is a seed year.Fraction of the seed production in non-seed-years.0.25 ( continued on next page )

85 Fig. 1 .
Fig.1.Parameter similarity among the tree species and provenances included in the dataset.The phylogram is based on an Agglomerative Hierarchical Clustering using a Gower distance matrix of 54 species parameters (i.e., those which could be meaningfully included in the analysis from the overall 66 parameters) for 150 tree species and provenances.The R code for the analysis can be accessed here: https://github.com/DominikThom/iLand-Species-Parameters.git .
Table species).For details on the use of the parameters in the iLand model logic see the online model documentation at https://iland-model.org .
* dbh ^ b) for branch biomass 2.3 finerootFoliageRatio The size of the fine root pool is defined relative to the size of the foliage pool (functional balance) i.e., fineRoots = poolsize foliage * finerootFoliageRatio.

Table 1 (
continued ) estPsiMin Minimum soil water potential for establishment (MPa); establishment probability is reduced linearly between estPsiMin ( p = 0), and field capacity ( p = 1, no limitation).Null or 0 disables soil water limitation.0estSOLthicknessEffect of thickness of the soil organic layer on establishment probability.Multiplier calculated as exp(-estSOLthickness * SOLdepthcm).